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--- |
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license: cc-by-4.0 |
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pretty_name: 'X-ray Reports Dataset' |
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language: |
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- en |
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tags: |
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- medical |
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- x-ray |
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- radiology |
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- chest |
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- reports |
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- image-to-text |
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- medical-imaging |
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- ai-research |
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task_categories: |
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- image-classification |
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- image-text-to-text |
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size_categories: |
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- 10K<n<100K |
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--- |
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# X-ray Reports Dataset |
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*This dataset contains high-quality (“A-grade”) anonymized X-ray images paired with radiology reports. It has been carefully curated, cleaned, and verified to ensure accuracy, completeness, and compliance with privacy standards (e.g., HIPAA/GDPR), making it suitable for high-stakes or research-grade model training.* |
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## Contact |
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For queries or collaborations related to this dataset, contact: |
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- anoushka@kgen.io |
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- abhishek.vadapalli@kgen.io |
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## Supported Tasks |
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- **Task Categories**: |
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- Image Classification |
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- Image-to-Text Generation |
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- **Supported Tasks**: |
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- Radiology report generation from X-ray images |
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- Multi-label classification of thoracic pathologies (e.g., pneumonia, cardiomegaly) |
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- Medical image analysis for triage support |
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- Cross-modal learning for vision-language models |
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- Feature extraction for diagnostic AI research |
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## Languages |
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- **Primary Language**: English (radiology reports) |
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## Dataset Creation |
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### Curation Rationale |
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This dataset was created to advance medical AI research by providing paired X-ray images and radiology reports for tasks like automated report generation and disease detection. It aims to support the development of robust, generalizable models for radiology. |
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### Source Data |
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- **Contributors**: De-identified data from hospital archives and public medical repositories |
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- **Collection Process**: Images sourced from PACS systems (2015–2023), reports authored by board-certified radiologists, anonymized to remove patient identifiers. |
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### Other Known Limitations |
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- **Size**: Limited to ~10,000 samples, which may restrict generalization |
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- **Demographic Bias**: Overrepresentation of adult urban patients; limited pediatric data |
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- **Image Quality**: Variations in X-ray resolution or equipment may affect consistency |
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- **Label Noise**: Potential errors in report-based labels extracted via NLP |
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## Intended Uses |
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### ✅ Direct Use |
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- Training and benchmarking models for radiology report generation |
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- Research in medical image-to-text generation |
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- Development of AI tools for radiology triage and decision support |
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- Academic research in medical imaging and natural language processing |
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### ❌ Out-of-Scope Use |
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- Clinical diagnosis without human radiologist oversight |
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- Commercial use without proper attribution or ethical review |
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- Applications violating patient privacy or medical ethics |
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- Real-time deployment without additional validation |
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## License |
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CC BY 4.0 |
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